Gemini has been watermarking for like a year and doesn't have the same problems.
> The same thing with metric cardinality: by default, only report metrics in a fully aggregated manner. But do tell the monitoring system how they can potentially be broken up if needed (i.e., which labels to add). How…
One strategy do to do that is to trace everything by default and select what to sample later, e.g. https://grafana.com/docs/grafana-cloud/observe-and-act/adapt...
Not true at all! Most of the HTTP APIs, and a good chunk of the webapps, that I've worked on can be defined as a combination of an API spec that carves out valid and invalid behaviors, and a set of behavioral tests for…
Writing a benchmark test is about 100x to 1000x easier than writing optimizations. A benchmark test can be as simple as: func Benchmark(b *testing.B) { for range b.N { runCodeUnderBenchmark() } } Actually optimizing the…
Gemini has been watermarking for like a year and doesn't have the same problems.
> The same thing with metric cardinality: by default, only report metrics in a fully aggregated manner. But do tell the monitoring system how they can potentially be broken up if needed (i.e., which labels to add). How…
One strategy do to do that is to trace everything by default and select what to sample later, e.g. https://grafana.com/docs/grafana-cloud/observe-and-act/adapt...
Not true at all! Most of the HTTP APIs, and a good chunk of the webapps, that I've worked on can be defined as a combination of an API spec that carves out valid and invalid behaviors, and a set of behavioral tests for…
Writing a benchmark test is about 100x to 1000x easier than writing optimizations. A benchmark test can be as simple as: func Benchmark(b *testing.B) { for range b.N { runCodeUnderBenchmark() } } Actually optimizing the…